EP3295126B1 - Procédé pour la détermination d'états d'un système au moyen d'un filtre d'estimation - Google Patents

Procédé pour la détermination d'états d'un système au moyen d'un filtre d'estimation Download PDF

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Publication number
EP3295126B1
EP3295126B1 EP16716010.0A EP16716010A EP3295126B1 EP 3295126 B1 EP3295126 B1 EP 3295126B1 EP 16716010 A EP16716010 A EP 16716010A EP 3295126 B1 EP3295126 B1 EP 3295126B1
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Prior art keywords
values
probability
deviation
states
data
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German (de)
English (en)
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EP3295126A1 (fr
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Uwe Herberth
Tim Martin
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Northrop Grumman Litef GmbH
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Northrop Grumman Litef GmbH
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D1/00—Measuring arrangements giving results other than momentary value of variable, of general application
    • G01D1/14—Measuring arrangements giving results other than momentary value of variable, of general application giving a distribution function of a value, i.e. number of times the value comes within specified ranges of amplitude
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
    • G01S19/49—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system whereby the further system is an inertial position system, e.g. loosely-coupled
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/36—Input/output arrangements for on-board computers
    • G01C21/3605—Destination input or retrieval

Definitions

  • state data is often used that has been determined using a different methodology.
  • the state data are typically subject to measurement errors, so that measured state data deviate from the actual states.
  • Estimated filters are often used to extrapolate the evolution of the system based on previous state measurements, and at the same time indicate a probability that the state of the system thus calculated matches the actual state.
  • the extrapolated states are compared and corrected after a certain time with measured state data.
  • Such a method is used in particular for determining position data of an object, such as a vehicle, a ship or an aircraft.
  • Relative and absolute positional data are collected from which the estimation filter provides a navigation solution, i. for navigation necessary position data determined.
  • a Kalman filter is often used as an estimation filter, which iteratively estimates position data and corrects it with newly measured data sets.
  • inertial measurement data i. Rotation rates and accelerations of the object
  • absolute position data i. the position relative to a fixed reference system.
  • the Kalman filter allows to evaluate these two types of position data for a common navigation solution.
  • the relative position data is used to estimate the position and the absolute data to correct the solution.
  • the correction step ie the update of the filter
  • the correction step is particularly computationally intensive. If data necessary for correction is continuously available, such a correction step is typically carried out at constant intervals. Otherwise, a correction is always carried out when the data required for the correction could be collected. This ensures a constant utilization of the processors used to calculate the correction and thus a constant energy consumption.
  • the continuous correction leads to a higher accuracy of the filter. However, if such a high accuracy is not needed or is the estimation filter due to prolonged operation so settled that even more frequent corrections bring no quality improvement more, is unnecessarily burdened by a continuous correction of the processor used to calculate the navigation solution and there is an unnecessarily high energy consumption.
  • the invention has for its object to provide a method for determining states of a system by means of an estimation filter, are reduced in the unnecessary burden of a computer processor and a resulting energy consumption.
  • a method for determining states of a system by means of an estimation filter comprises: determining first state values by calculating mean values of respective probability distributions for each of the states by the estimation filter; Calculating a deviation probability that the first state values deviate from the actual states of the system by the estimation filter; Measuring the states of the system as state data.
  • the first state values are corrected by means of the state data.
  • a quality of the estimation is calculated via the deviation probability.
  • the estimates are only corrected if the quality of the estimate falls below a certain threshold. This is supposed to be the case if the deviation probability, which indicates the distance of the estimate from the actual state of the system, becomes greater than a certain limit value.
  • the limit value can be fixed or dynamically determinable. As a result, the quality of the entire status determination can be set by means of the limit value.
  • the threshold can be defined differently for different ways of determining the deviation probability.
  • the number of correction steps can thereby be reduced, whereby the load of a processor used to calculate the states can be reduced.
  • the reduced power consumption also lowers the processor's power consumption.
  • the first state value may be determined in a first time step.
  • second state values based on the first state values are determined in a second time step following the first time step.
  • the second state values in the second time step are determined based on the corrected first state values if and only if the deviation probability is greater than the limit value.
  • the estimate in the second time step is based on these corrected state values.
  • the estimation in the second time step is based on the uncorrected first state values estimated in the first time step.
  • the estimation filter can be a Kalman filter and the states of the system can determine a position of an object. Then, the state values are position values indicating the position and the measured state data are position data.
  • UAVs unmanned aerial vehicles
  • UASs unmanned aerial systems
  • the limit may be, for example, the maximum allowable horizontal position error his.
  • UAV mission and navigation computers can be merged, saving weight and extending mission or deployment times.
  • the position data may include an absolute position of the object and relative position changes of the object.
  • the relative position changes are used to determine the mean of the probability distribution for the position, whereas the absolute position is used to correct position values.
  • the different measured position data can be optimally combined.
  • the relative position changes such as rotation rates or accelerations of the object, are used to estimate the further movement of the object based on the previous estimates.
  • the absolute position data given with respect to a particular reference system such as latitude and longitude, can be used to correct the estimate.
  • measurement errors of the absolute position data for determining the position are of less importance, since they are not recognized as measurement errors only if the estimation has similar errors.
  • two different types of position determination are combined with each other to alternately reduce the influence of measurement errors.
  • specifying that a correction should only be made if the quality of the estimate becomes too poor will ensure that the energy used in determining the position remains low.
  • the position data can be measured by means of a satellite navigation system and a linear acceleration and / or yaw rate sensor. This ensures that positional data collection can be automated, eliminating the need for constant human surveillance or human intervention.
  • a micro-electro-mechanical sensor can be used.
  • MEMS micro-electro-mechanical sensor
  • fiber-optic sensors eg fiber-optic gyros
  • ring laser gyros e.g., ring laser gyros
  • the deviation probability can be determined based on a covariance matrix of the probability distribution.
  • covariance matrices are automatically generated in an estimation filter, such as a Kalman filter.
  • the calculation of the deviation probability can be included in existing filters without additional process steps, so that no additional processor load or additional time loss occurs.
  • the deviation probability may also be based on a residual, i. the comparison of the estimated and the measured position. This allows a more flexible handling of the quality setting, as under certain circumstances it may be necessary not to rely solely on the covariance matrices calculated in the filter, but to have a second basis for the estimation of the estimation quality.
  • the alternative or additional use of a residuum thus increases the flexibility of the use of the method.
  • An apparatus for determining a position of an object comprises a measuring unit which is suitable for measuring position data and a computing unit with an estimation filter which is suitable for determining first position values by calculating an average value of a probability distribution for the position and calculating a deviation probability therefor, that the first position values deviate from the actual position of the object.
  • the arithmetic unit is suitable for precisely correcting the first position values by means of the position data if the deviation probability is greater than the limit value.
  • This device ensures that a method according to the invention can be carried out by means of which the position of an object can be determined without the arithmetic unit, e.g. a processor is unnecessarily burdened and unnecessary power consumption occurs.
  • the measuring unit may comprise a satellite navigation system and a linear acceleration and / or yaw rate sensor.
  • the linear acceleration and / or yaw rate sensor is suitable for measuring relative position changes that are used to determine the mean value of the probability distribution for the position.
  • the satellite navigation system is capable of measuring an absolute position used to correct position values. This ensures that the device uses the widest possible basis of measured values for determining the position, which is determined by means of the Schersonfilter optimally combined. Thereby, the required accuracy of the position determination can be realized simultaneously with a reduced power consumption.
  • An unmanned aerial vehicle or an unmanned aerial vehicle system may operate according to the methods described above.
  • the unmanned aerial vehicle or the unmanned aerial vehicle system may comprise one of the devices described above. This ensures that the navigation solutions used by these aircraft or systems are sufficiently accurate while minimizing the energy used to determine the navigation solution. Low energy consumption also allows long mission or deployment times of the aircraft or systems to be realized.
  • FIG. 1A shows an apparatus 100 for determining states of a system.
  • the device 100 has a measuring unit 110 and a computing unit 150.
  • the measuring unit 110 is capable of measuring various states of the system and outputting as state data.
  • the computing unit 150 e.g. a computer or processor calculates the states of the system based on the state data measured by the measurement unit 110.
  • the arithmetic unit 150 uses an estimation filter, i. an algorithm with which state values can be estimated.
  • the estimation filter calculates the mean of a probability distribution for the states based on the state data. At the same time, the estimation filter determines the probability that the calculated mean values for the states deviate from the actual states of the system.
  • a quality value can be formed, which is the greater, the greater the deviation.
  • the greater the deviation the smaller the quality value. Then the quality value is proportional to the quality of the measurement.
  • the state values estimated by the estimation filter are corrected.
  • the state values will be corrected if the deviation probability is greater than a predetermined limit.
  • the arithmetic unit 150 makes corrections to the estimated state values only when it is actually necessary. As a result, a utilization of the computing unit 150 and thus its power consumption is reduced.
  • the states determined by the device 100 are abstract quantities, e.g. purely mathematical parameters or parameters of a mathematical model, such as an economic model.
  • the states are concrete parameters of a system, e.g. concrete measurement results.
  • device 100 is used to determine a position of an object.
  • the measuring unit 110 measures position data and the estimation filter of the arithmetic unit 150 determines position values by calculating an average value of a probability distribution for the position. If the quality of this estimation is too low, ie a deviation probability which describes the deviation of the position values from the actual position is greater as a threshold value, the estimation filter corrects the position values by means of further position data measured by the measuring unit 110.
  • FIG. 1B shows the device 100 in further detail.
  • the measuring unit 110 has a satellite navigation system 120 and a linear acceleration and / or yaw rate sensor 130.
  • the satellite navigation system 120 may be used to determine an absolute position of the object on the earth's surface by triangulation between a number of satellites orbiting in the earth's orbit. The satellite navigation system 120 thus measures an absolute position of the object with respect to the earth's surface.
  • the linear acceleration and / or rotation rate sensor 130 measures relative position changes of the object, such as rotation rates or accelerations of the object. It is therefore possible, by means of the data measured by the linear acceleration and / or rotation rate sensor 130, to calculate the movement of the object in space by integration of the equations of motion.
  • the linear acceleration and / or yaw rate sensor 130 may be, for example, a micro-electro-mechanical sensor (MEMS).
  • MEMS micro-electro-mechanical sensor
  • fiber-optic sensors e.g., fiber optic gyroscopes
  • ring laser gyros ring laser gyros
  • the relative position changes are used to estimate the position of the object. Since every measurement is subject to a measurement error, it is not possible to calculate the position exactly. It can only be estimated in terms of measurement accuracy. As the various measurement errors accumulate over time, the estimation also becomes increasingly inaccurate over time. It is therefore necessary to correct the estimate to meet accuracy requirements.
  • the absolute position data are used, which are compared with the estimated position values calculated by the estimation filter. In this way it is possible to compensate for measurement errors of the relative position changes by means of absolute position measurements.
  • the absolute position measurement can be faulty. However, this is only significant if the estimate based on the relative position changes has the same error tendency. Since measurement errors vary statistically, this will usually not be the case. Therefore, the estimation for correcting measurement errors of the absolute position measurement can also be used.
  • the estimation filter automatically calculates the variances and covariances of the means. The larger the entries of the resulting covariance matrix, the worse the estimate will be. If the entries are small, it is not necessary to correct the estimate. This ensures that no unnecessary correction steps are carried out, unnecessarily burdening the arithmetic unit 150 and leading to unnecessary energy consumption.
  • a residual may be used as the deviation probability, which indicates the deviation of the estimated data from the position determined by measurement. This can also be used to prevent unnecessary correction steps from being carried out if the estimate is still good enough.
  • the device 100 can determine states of a system.
  • FIG. 2 shows a flowchart of an embodiment of a method for determining state values of a system.
  • state values of a system are determined by calculating an average of a probability distribution for the states.
  • state values are estimated based on a probability distribution.
  • an estimation filter such as a Kalman filter.
  • the probability distribution for the state values to be estimated results from previous measurements of the states which are subject to measurement errors.
  • a deviation probability is calculated.
  • the probability of deviation is greater, the greater the probability that the state values deviate from the actual states of the system.
  • the greater the deviation probability the less accurate the estimate and the lower the quality of the estimate. The quality of the estimate must therefore be improved as soon as the deviation probability becomes too large.
  • a quality value is calculated which is small for a large probability of large distances of the state values from the actual states. That is, the smaller the quality value, the lower the quality of the estimate. Then the quality must be improved if the quality value is too small.
  • the deviation probability is compared to a threshold. It is determined whether the deviation probability is greater than the limit value. If this is not the case (No), the state values are not corrected. Alternatively, the quality value is compared. Then there is no correction if the quality value is sufficiently large.
  • the states of the system are measured again and the state values are corrected by means of the measured states.
  • the states of the system are already measured previously and used to correct the state values only when it has been determined that the deviation probability is greater than the limit.
  • This procedure ensures that state values are only corrected if necessary. This makes it possible to have a computing unit involved in the estimation and correction, e.g. a computer or processor, to relieve and thus reduce energy consumption.
  • a computing unit involved in the estimation and correction e.g. a computer or processor, to relieve and thus reduce energy consumption.
  • FIG. 3 shows a schematic flow diagram of another embodiment of a method for determining states of a system.
  • the method according to FIG. 3 largely corresponds to the method FIG. 2 , Therefore, only the steps of the method which differ are explained in more detail.
  • the methods differ in that in the method according to FIG. 3 the state values are iteratively estimated in a step S300. If, in fact, the deviation probability is greater than the limit value, as in FIG. 2 described method, the state values by means of other measured states corrected. The corrected states are then used to again estimate state values based on the corrected state values of the previous time step in a further time step.
  • the correction step is skipped and the state values are estimated in the following time step based on the uncorrected state values determined in the previous time step.
  • FIG. 4 shows a schematic flow diagram of an embodiment of a method according to the invention, in which a state of a system, a position of an object, such as a vehicle, ship or aircraft, is determined.
  • the rate of rotation and / or the linear acceleration of the object is measured at S400.
  • an inertial sensor such as a micro-electro-mechanical sensor or designed as a fiber optic gyros or ring laser gyroscope rotation rate sensors.
  • These relative position changes enter a Kalman filter at S410 where position values are determined by calculating an average of a probability distribution for the position.
  • a deviation probability is calculated, ie the probability of a deviation of the position values from the actual position. This deviation probability is compared with a limit at S430. If the deviation probability is greater (Yes) than the previously determined limit value, the previously estimated position values are corrected.
  • the absolute position of the object is measured.
  • a satellite navigation system is used that indicates the position of the object with respect to a fixed reference system, such as the latitudes and longitudes of the earth.
  • the position value is corrected by the measured absolute position. The corrected position values serve as a starting point for the estimation of the position in the next time step.
  • the correction of the position values is omitted and the Kalman filter goes directly to the next estimate, which is then based on the position values determined in the previous time step and the newly measured relative position changes.

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Navigation (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Claims (11)

  1. Procédé pour la détermination d'états d'un système au moyen d'un filtre d'estimation, comprenant :
    la détermination de premières valeurs d'état par calcul de valeurs moyennes de lois de probabilité respectives pour chacun des états par le filtre d'estimation ;
    le calcul d'une probabilité d'écart pour que les premières valeurs d'état divergent des états réels du système, par le filtre d'estimation ;
    la mesure des états du système en tant que données d'état ; et
    puis, lorsque la probabilité d'écart est supérieure à une valeur limite, la correction des premières valeurs d'état au moyen des données d'état.
  2. Procédé selon la revendication 1, dans lequel
    les premières valeurs d'état sont déterminées dans un premier incrément de temps ;
    puis, lorsque la probabilité d'écart est inférieure ou égale à la valeur limite, dans un deuxième incrément de temps suivant le premier incrément de temps, des deuxièmes valeurs d'état sont déterminées sur la base des premières valeurs d'état ; et
    puis, lorsque la probabilité d'écart est supérieure à la valeur limite, dans le deuxième incrément de temps, les deuxièmes valeurs d'état sont déterminées sur la base des premières valeurs d'état corrigées.
  3. Procédé selon l'une des revendications précédentes, dans lequel
    le filtre d'estimation est un filtre de Kalman ;
    les états du système déterminent une position d'un objet ;
    les valeurs d'état sont des valeurs de position, qui indiquent la position ; et
    les données d'état mesurées sont des données de position.
  4. Procédé selon la revendication 3, dans lequel
    les données de position comprennent une position absolue de l'objet et des changements de position relatifs de l'objet ;
    les changements de position relatifs sont utilisés pour déterminer la valeur moyenne de la loi de probabilité pour la position ; et
    la position absolue est utilisée pour corriger des valeurs de position.
  5. Procédé selon l'une des revendications 3 à 4, dans lequel
    les données de position sont mesurées au moyen d'un système de navigation par satellite et d'un capteur d'accélération linéaire et/ou de vitesse de rotation.
  6. Procédé selon l'une des revendications 3 à 5, dans lequel
    un capteur micro-électromécanique (MEMS), un capteur à fibre optique ou un gyrolaser annulaire est utilisé pour la mesure des données de position.
  7. Procédé selon l'une des revendications précédentes, dans lequel la probabilité d'écart est déterminée sur la base d'une matrice de covariance de la loi de probabilité.
  8. Procédé selon l'une des revendications précédentes, dans lequel la probabilité d'écart est déterminée sur la base d'au moins un résidu.
  9. Dispositif (100) pour la détermination d'une position d'un objet, comprenant
    une unité de mesure (110) adaptée pour la mesure de données de position ; et
    une unité de calcul (150) avec un filtre d'estimation adaptée pour
    la détermination de premières valeurs de position par calcul de valeurs moyennes de lois de probabilité respectives pour la position par le filtre d'estimation ;
    le calcul d'une probabilité d'écart pour que les premières valeurs de position divergent de la position réelle de l'objet, par le filtre d'estimation ; et
    puis, lorsque la probabilité d'écart est supérieure à une valeur limite, la correction des premières valeurs de position au moyen des données de position.
  10. Dispositif (100) selon la revendication 9, dans lequel
    l'unité de mesure (110) comprend un système de navigation par satellite (120) et un capteur d'accélération linéaire et/ou de vitesse de rotation (130) ;
    le capteur d'accélération linéaire et/ou de vitesse de rotation (130) est adapté pour mesurer des changements de position relatifs, qui sont utilisés pour déterminer la valeur moyenne de la loi de probabilité pour la position ; et dans lequel
    le système de navigation par satellite (120) est adapté pour mesurer une position absolue qui est utilisée pour corriger des valeurs de position.
  11. Drone avec un dispositif selon la revendication 9 ou 10.
EP16716010.0A 2015-05-08 2016-03-23 Procédé pour la détermination d'états d'un système au moyen d'un filtre d'estimation Active EP3295126B1 (fr)

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DE102015107265.3A DE102015107265A1 (de) 2015-05-08 2015-05-08 Verfahren zum Bestimmen von Zuständen eines Systems mittels eines Schätzfilters
PCT/EP2016/056389 WO2016180566A1 (fr) 2015-05-08 2016-03-23 Procédé pour la détermination d'états d'un système au moyen d'un filtre d'estimation

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EP (1) EP3295126B1 (fr)
CN (1) CN107580684B (fr)
DE (1) DE102015107265A1 (fr)
IL (1) IL255320B (fr)
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TWI636236B (zh) 2018-09-21
WO2016180566A1 (fr) 2016-11-17
IL255320A0 (en) 2017-12-31
CN107580684A (zh) 2018-01-12
DE102015107265A1 (de) 2016-11-10
US20180128619A1 (en) 2018-05-10
IL255320B (en) 2018-05-31
CN107580684B (zh) 2019-03-26
TW201704720A (zh) 2017-02-01
EP3295126A1 (fr) 2018-03-21
US10088319B2 (en) 2018-10-02

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